Wetscapes: Restoring and maintaining peatland landscapes for sustainable futures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peatlands are among the world's most carbon-dense ecosystems and hotspots of carbon storage. Although peatland drainage causes strong carbon emissions, land subsidence, fires and biodiversity loss, drainage-based agriculture and forestry on peatland is still expanding on a global scale. To maintain and restore their vital carbon sequestration and storage function and to reach the goals of the Paris Agreement, rewetting and restoration of all drained and degraded peatlands is urgently required. However, socio-economic conditions and hydrological constraints hitherto prevent rewetting and restoration on large scale, which calls for rethinking landscape use. We here argue that creating integrated wetscapes (wet peatland landscapes), including nature preserve cores, buffer zones and paludiculture areas (for wet productive land use), will enable sustainable and complementary land-use functions on the landscape level. As such, transforming landscapes into wetscapes presents an inevitable, novel, ecologically and socio-economically sound alternative for drainage-based peatland use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it